Robots rehearse first: simulation, benchmarks and coordinated flight move together
NVIDIA showed a full rehearsal stack at CoRL, a new benchmark grades how robots handle unseen objects, and Shield flew coordinated aircraft while Pudu put robots to work in low light.
- NVIDIA introduced GR00T 2, an open world action model built on NVIDIA Cosmos, at CoRL, trained on 100,000 hours of human egocentric data, and the company says it completed 40 of 50 evaluated tasks without task-specific demonstrations. Self-reported m:…2860
- Isaac Lab 3.0 with Newton reached up to 2.5 times the training throughput of Isaac Lab 2.3 with PhysX in a vision-based dexterous manipulation benchmark, according to NVIDIA. Self-reported m:…2860
- On DexBench, NVIDIA GR00T 1.7 managed a 33.6% overall success rate across 1,680 episodes, falling from 44.8% under training conditions to 21.7% with unseen objects. m:…1836
- Shield AI says its Hivemind software enabled multiple Air Force collaborative combat aircraft to fly in formation as a coordinated team, the second of two firsts for the programme in three weeks. Self-reported m:…3372
- Pudu Robotics says cleaning a 2,000-square-metre area with its cleaning robots takes about 15 litres of water, against about 100 litres with conventional equipment, and about one-tenth the cleaning solution. Self-reported m:…2579
What NVIDIA showed at CoRL
At CoRL, NVIDIA introduced GR00T 2, an open world action model built on NVIDIA Cosmos, and the company says the model was trained on 100,000 hours of human egocentric data. The point of that training, in NVIDIA's account, is dexterous manipulation: robots learning to handle objects rather than simply moving through a space. The company says GR00T 2 completed 40 of 50 evaluated tasks without task-specific demonstrations, which is the kind of result that matters to a founder deciding whether a robot can be put to work on a task it was never shown directly. m:…2860
The other half of the announcement was the training ground itself. Isaac Lab 3.0 lets researchers switch between the PhysX and Newton physics solvers, and NVIDIA says Isaac Lab 3.0 with Newton reached up to 2.5 times the training throughput of Isaac Lab 2.3 with PhysX in a vision-based dexterous manipulation benchmark. Skild AI announced STEEL, a rigid-body physics solver built on Newton to speed up robot training with Isaac Lab. This suggests the bottleneck is no longer a single model: it is how many simulated attempts a team can run, and how fast, before the robot ever touches a real object. m:…2860
NVIDIA also pointed to work that stretches simulation further. The company says Success-Guided Sampling allows robot training to scale beyond a million parallel environments, and AlphaNIST achieved 98% training success on 20 tasks using Isaac Lab. UC Berkeley's Graph-as-Policy framework uses agents to connect individual skills into robot programs, and Ambi Robotics, Bosch Research and Siemens are exploring it for agentic robotics workflows. The pattern here is consistent: the rehearsal is getting bigger, faster and more automated, and industrial users are testing whether that rehearsal can be turned into programmes a robot can actually run. m:…2860
The benchmark that grades the rehearsal
A rehearsal is only useful if someone can grade it, and that is where DexBench comes in. RLWRLD developed DexBench on NVIDIA Isaac Lab-Arena to evaluate bimanual robotic dexterity, and the benchmark measures manipulation for manufacturing, logistics and service environments. DexBench features 15 tasks across four scenes spanning retail, logistics, manufacturing and hospitality, and it evaluates generalisation across object, physics and spatial dimensions. The breadth matters: a robot that only works with the exact object it was trained on is a demonstration, not a product. m:…2860 m:…1836
The first public score is sobering. DexBench is now live on Manifold, which automates evaluation by distributing simulation workloads across parallel GPU compute, and NVIDIA GR00T 1.7 achieved a 33.6% overall success rate on DexBench, succeeding in 564 of 1,680 episodes. Success fell from 44.8% under training conditions to 21.7% with unseen objects, so new objects cost the policy dearly. For a founder, that gap is the product question: the rehearsal works until the world changes the object, and closing that gap is where the next round of work has to go. m:…1836
From simulation to the real world
The same week also showed what happens after the rehearsal, when systems have to work together in the open. Shield AI says its Hivemind software enabled multiple Air Force collaborative combat aircraft to fly in formation as a coordinated team, and the company says it achieved two firsts for the U.S. Air Force's collaborative combat aircraft programme in three weeks. The earlier milestone was an F-22 pilot directing an FQ-44A Fury in flight. This is coordination, not a single vehicle showing off: several aircraft flying as a team, with a human pilot directing one of them. m:…3372
Pudu Robotics showed a quieter version of the same shift, at ground level. The company joined the Lights Out for Migratory Birds campaign at Shenzhen Bay Park on November 6, 2026, and its D5 quadruped robot, equipped with a dual-light inspection gimbal, can support equipment inspections in low-light environments. The PUDU MT1 Max autonomous cleaning robot cleaned the outdoor venue, and Pudu says cleaning a 2,000-square-metre area with its robots takes about 15 litres of water, against about 100 litres with conventional equipment, and about one-tenth the cleaning solution. Shenzhen Bay is a stopover and wintering site along the East Asian-Australasian Flyway, and artificial light at night can disrupt birds' navigation, migration patterns and natural rhythms, so working in low light is itself part of the job. m:…2579
There is also a smaller piece of the puzzle that connects rehearsal to human hands. NVIDIA Holoscan 5 supports the creation of gravity-compensation loops for robot arms, and those loops let a person guide a robot arm by hand while its motors supply just enough torque to support its weight and keep the robot upright. Building such a loop requires continuously calculating the inverse dynamics of the arm, and the company presents it as a starting point for robots that learn by demonstration, manipulate objects, or work alongside people. In other words, the rehearsal loop has a human entrance: show the robot once, by hand, and let simulation take it from there. m:…4399
What is still unresolved
- Whether agentic robot programmes will prove reliable over the long term in unstructured environments.
- When DexBench physics generalisation will reach general availability.
- Whether GR00T 2's out-of-the-box task performance holds up outside the evaluated tasks.
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